Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening

RATIONALE AND OBJECTIVES: A computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening was developed. MATERIALS AND METHODS: Our scheme is based on a difference-image technique for enhancing the lung nodules and suppressing the majority of background normal structures. The difference image for each computed tomography image was obtained by subtracting the nodule-suppressed image processed with a ring average filter from the nodule-enhanced image with a matched filter. The initial nodule candidates were identified by applying a multiple-gray level thresholding technique to the difference image, where most nodules were well enhanced. A number of false-positives were removed first in entire lung regions and second in divided lung regions by use of the two rule-based schemes on the localized image features related to morphology and gray levels. Some of the remaining false-positives were eliminated by use of a multiple massive training artificial neural network trained for reduction of various types of false-positives. This computerized scheme was applied to a confirmed cancer database of 106 low-dose computed tomography scans with 109 cancer lesions for 73 patients obtained from a lung cancer screening program in Nagano, Japan. RESULTS: This computed-aided diagnosis scheme provided a sensitivity of 83% (91/109) for all cancers with 5.8 false-positives per scan, which included 84% (32/38) for missed cancers with 5.9 false-positives per scan. CONCLUSION: This computerized scheme may be useful for assisting radiologists in detecting lung cancers on low-dose computed tomography images for lung cancer screening.     

Lumpy silicone-injected breasts: enhanced MRI and microscopic correlation

Clinical images emphasizing on magnetic resonance imaging (MRI) characteristics of silicone-injected breasts with microscopic correlation were presented. A total of 16 patients with a history of silicone injection breast augmentation received MRI examinations due to palpable masses were reviewed. MRI enables the demonstration of the characteristic MR intensities in silicone-injected breasts. Four breast cancers were accurately detected by the enhancement technique including the cancers admixing within the fibrosis and silicone granuloma. This article can document the excellent ability of MRI with high microscopic correlation in examining these clinical, mammographic and ultrasonographic difficult cases. Understanding the MRI features is helpful to approach these patients. On the other hand, these illustrations indicated the importance of enhancement technique in detecting the cancers and in differentiating the angiogenic lesions from nonenhanced silicone granulomas.     

Radiomic analysis of DCE-MRI for prediction of response to neoadjuvant chemotherapy in breast cancer patients

ObjectivesTo enhance the accurate prediction of the response to neoadjuvant chemotherapy (NAC) in breast cancer patients by using a quantitative analysis of dynamic enhancement magnetic resonance imaging (DCE-MRI).Materials and methodsA dataset of 57 cancer patients with breast DCE-MR images acquired before NAC was used. Among them, 47 patients were Responders, and 10 patients were non-Responders based on the RECIST criteria. The breast regions were segmented on the MR images, and a total of 158 radiomic features were computed to represent the morphologic, dynamic, and the texture of the tumors as well as the background parenchymal features. The optimal subset of features was selected using evolutionary based Wrapper Subset Evaluator. The classifier was trained and tested using a leave-one-out cross-validation (LOOCV) method to classify Responder and non-Responder cases. The area under a receiver operating characteristic curve (AUC) was computed to assess the classifier performance. An additional independent dataset with 46 patients was also included to validate the results.ResultsThe evolutionary algorithm (EA)-based method identified optimal subsets comprising 12 image features that were fit for classification for the main cohort. Following the same feature selection procedure, the independent validation dataset produced 11 image features, 7 of which were identical to those from the main cohort. The classifier based on the features yield a LOOCV AUC of 0.910 and 0.874 for the main and the reproducibility study cohort, respectively. If the optimal features in the main cohort were utilized to test performance on the reproducibility cohort, the classifier generated an AUC of 0.713. While the features developed in the reproducibility cohort were applied to test the main cohort, the classifier achieved an AUC of 0.683. The AUC of the averaged receiver operating characteristic (ROC) curve for the two data cohort was 0.703.ConclusionsThis study demonstrated that quantitative analyses of radiomic features from pretreatment breast DCE-MRI data could be used as valuable image markers that are associated with tumor response to NAC.     

Improved mammographic interpretation of masses using computer-aided diagnosis

The aim of this study was to evaluate the effectiveness of computerized image enhancement, to investigate criteria for discriminating benign from malignant mammographic findings by computer-aided diagnosis (CAD), and to test the role of quantitative analysis in improving the accuracy of interpretation of mass lesions. Forty sequential mammographically detected mass lesions referred for biopsy were digitized at high resolution for computerized evaluation. A prototype CAD system which included image enhancement algorithms was used for a better visualization of the lesions. Quantitative features which characterize the spiculation were automatically extracted by the CAD system for a user-defined region of interest (ROI). Reference ranges for malignant and benign cases were acquired from data generated by 214 known retrospective cases. The extracted parameters together with the reference ranges were presented to the radiologist for the analysis of 40 prospective cases. A pattern recognition scheme based on discriminant analysis was trained on the 214 retrospective cases, and applied to the prospective cases. Accuracy of interpretation with and without the CAD system, as well as the performance of the pattern recognition scheme, were analyzed using receiver operating characteristics (ROC) curves. A significant difference (p < 0.005) was found between features extracted by the CAD system for benign and malignant cases. Specificity of the CAD-assisted diagnosis improved significantly (p < 0.02) from 14 % for the conventional assessment to 50 %, and the positive predictive value increased from 0.47 to 0.62 (p < 0.04). The area under the ROC curve (A_z) increased significantly (p < 0.001) from 0.66 for the conventional assessment to 0.81 for the CAD-assisted analysis. The A_z for the results of the pattern recognition scheme was higher (0.95). The results indicate that there is an improved accuracy of diagnosis with the use of the mammographic CAD system above that of the unassisted radiologist. Our findings suggest that objective quantitative features extracted from digitized mammographic findings may help in differentiating between benign and malignant masses, and can assist the radiologist in the interpretation of mass lesions. Received: 27 July 1998; Accepted: 8 October 1998     

Denoising and enhancing digital mammographic images for visual screening.

Dense regions in digital mammographic images are usually noisy and have low contrast, and their visual screening is difficult. This paper describes a new method for mammographic image noise suppression and enhancement, which can be effective particularly for screening image dense regions. Initially, the image is preprocessed to improve its local contrast and the discrimination of subtle details. Next, image noise suppression and edge enhancement are performed based on the wavelet transform. At each resolution, coefficients associated with noise are modelled by Gaussian random variables; coefficients associated with edges are modelled by Generalized Laplacian random variables, and a shrinkage function is assembled based on posterior probabilities. The shrinkage functions at consecutive scales are combined, and then applied to the wavelets coefficients. Given a resolution of analysis, the image denoising process is adaptive (i.e. does not require further parameter adjustments), and the selection of a gain factor provides the desired detail enhancement. The enhancement function was designed to avoid introducing artifacts in the enhancement process, which is essential in mammographic image analysis. Our preliminary results indicate that our method allows to enhance local contrast, and detect microcalcifications and other suspicious structures in situations where their detection would be difficult otherwise. Compared to other approaches, our method requires less parameter adjustments by the user.     

Automated computerized scheme for detection of unruptured intracranial aneurysms in three-dimensional magnetic resonance angiography

RATIONALE AND OBJECTIVES: A computerized scheme for automated detection of unruptured intracranial aneurysms in magnetic resonance angiography was developed based on the use of a three-dimensional selective enhancement filter for dots (aneurysms). MATERIALS AND METHODS: Twenty-nine cases with 36 unruptured aneurysms (diameter, 3 to 26 mm; mean, 6.6 mm) and 31 non-aneurysm cases were used in this study. The isotropic 3-dimensional magnetic resonance angiography images with 400 x 400 x 128 voxels (voxel size, 0.5 mm) were processed by use of the selective enhancement filter. The initial candidates were identified by use of a multiple gray-level thresholding technique on the dot-enhanced images and a region-growing technique with monitoring some image features. All candidates were classified into four types of candidates according to the size and local structures based on the effective diameter and skeleton image of each candidate (ie, large candidates and three types of small candidates including short-branch type, single-vessel type, and bifurcation type). In each group, a number of false-positives were removed by use of different rules on localized image features related to gray levels and morphology. Linear discriminant analysis was used for further removal of false-positives. RESULTS: With this computer-aided diagnostic scheme, all of 36 aneurysms were correctly detected with 2.4 false-positives per patient based on a leave-one-out-by-patient test method. CONCLUSION: This computer-aided diagnostic system would be useful in assisting radiologists for the detection of intracranial aneurysms in magnetic resonance angiography.     

Computer-aided detection schemes: the effect of limiting the number of cued regions in each case

OBJECTIVE: We assessed performance changes of a mammographic computer-aided detection scheme when we restricted the maximum number of regions that could be identified (cued) as showing positive findings in each case. MATERIALS AND METHODS: A computer-aided detection scheme was applied to 500 cases (or 2,000 images), including 300 cases in which mammograms showed verified malignant masses. We evaluated the overall case-based performance of the scheme using a free-response receiver operating characteristic approach, and we measured detection sensitivity at a fixed false-positive detection rate of 0.4 per image after gradually reducing the maximum number of cued regions allowed for each case from seven to one. RESULTS: The original computer-aided detection scheme achieved a maximum case-based sensitivity of 97% at 3.3 false-positive detected regions per image. For a detection decision score set at 0.565, the scheme had a 79% (237/300) case-based sensitivity, with 0.4 false-positive detected regions per image. After limiting the number of maximum allowed cued regions per case, the false-positive rates decreased faster than the true-positive rates. At a maximum of two cued regions per case, the false-positive rate decreased from 0.4 to 0.21 per image, whereas detection sensitivity decreased from 237 to 220 masses. To maintain sensitivity at 79%, we reduced the detection decision score to as low as 0.36, which resulted in a reduction of false-positive detected regions from 0.4 to 0.3 per image and a reduction in region-based sensitivity from 66.1% to 61.4%. CONCLUSION: Limiting the maximum number of cued regions per case can improve the overall case-based performance of computer-aided detection schemes in mammography.     

Computerized localization of breast lesions from two views. An experimental comparison of two methods.

RATIONALE AND OBJECTIVES: The authors compared two computerized methods, the arc and cartesian straight-line, for the localization of breast lesions in two mammographic views. METHODS: A total of 571 craniocaudal and 571 mediolateral oblique matched mammographic image pairs (or 1142 individual images) depicting 290 pathology-verified masses on both views were selected from our image database. Using a previously developed computer-aided detection scheme, all 290 masses and 3992 suspicious but negative regions were identified. After pairing all identified regions from both views, all masses (true-positive-true-positive matched pairs) and a total of 10330 false-positive pairs (including false-positive-false-positive, true-positive-false-positive, and false-positive-true positive pairs) were assessed as to their position in relation to the nipple using both the arc and the cartesian straight-line methods. Receiver operating characteristic methodology was used to evaluate the performance levels for each method in determining, based solely on location, whether a pair of suspicious regions represented a true mass or a false-positive combination. RESULTS: The areas under the receiver operating characteristic curves (Az) were 0.79 and 0.78 for the arc and cartesian straight-line methods, respectively. The difference between the two techniques (as measured by Az) was not statistically significant (P > 0.99). CONCLUSIONS: These preliminary results demonstrated that the two methods are comparable in identifying true masses from triangulated observations on two views. However, the arc method is somewhat favorable because only the nipple location is required for localization.     

Computer-aided detection of ischemic lesions related to subcortical vascular dementia on magnetic resonance images

RATIONALE AND OBJECTIVES: The purpose of this study was to develop an automated method for detection of the hyperintense ischemic lesions related to subcortical vascular dementia based on conventional magnetic resonance images (T1-weighted, T2-weighted, and fluid-attenuated inversion-recovery images [FLAIR]). MATERIALS AND METHODS: Our proposed method was based on subtraction between the T1-weighted image and the FLAIR image. First, a brain region was extracted by an automated thresholding technique based on a linear discriminant analysis for a pixel value histogram. Next, for enhancement of ischemic lesions, the T1-weighted image was subtracted from the fluid-attenuated inversion-recovery image. Ischemic lesion candidates were identified using a multiple gray-level thresholding technique and a feature-based region-growing technique on the subtraction image. Finally, an artificial neural network trained with 15 image features of the ischemic candidates was used to remove false-positives. We applied our method to nine patients with vascular dementia (age range, 64-94 years, mean age, 69.4 years; four males and five females), who were scanned on a 1.5-T magnetic resonance unit. RESULTS: Our method achieved a sensitivity of 90% with 4.0 false-positives per slice in detection of ischemic lesions. The overlap measure between ischemic lesion areas obtained by our method and a neuroradiologist was 60.7% on average. The ratio of ischemic lesion area to the whole brain area obtained by our method correlated with that determined by a neuroradiologist with a correlation coefficient of 0.911. CONCLUSION: Our preliminary results suggest that the proposed method may have feasibility for evaluation of the ischemic lesion area ratio.     

Computer-aided diagnosis of masses with full-field digital mammography

RATIONALE AND OBJECTIVES: The authors developed and evaluated a method of computer-aided diagnosis (CAD) for mass detection with full-field digital mammography (FFDM). MATERIALS AND METHODS: The new CAD method for FFDM employs adaptive, nonlinear multiscale processing and hybrid classification methods. The major strategies are (a) to "standardize" the mammographic image before it is input to the analysis modules, (b) to adapt the segmentation of suspicious regions adapt to accommodate different characteristics of masses and mammograms, and (c) to use combined "hard" and "soft" decision making in discriminating between mass and normal tissue regions. Two data sets of diagnostic FFDM mammograms were used. The training data set includes 36 normal and 24 abnormal mammograms (34 masses), and the testing data set includes 24 normal and 10 abnormal mammograms (10 masses). The tumors in this diagnostic database were more subtle and difficult to detect than those in screening databases the authors have used before. RESULTS: With the limited database and a partial optimization, a sensitivity of 91% was obtained in training, with a false-positive rate of 3.21 per image. At this trained operating point of the CAD system, six of 10 subtle masses were detected in testing. CONCLUSION: The CAD algorithms developed in screen-film mammography can be modified for FFDM. More data analysis and system optimization and evaluation will be needed before CAD can be integrated efficiently into the performance of FFDM.     

Tumour detection rate of a new commercially available computer-aided detection system

The aim of this study was to determine the tumour detection rate and false positive rate of a new mammographic computer-aided detection system (CAD) in order to assess its clinical usefulness. The craniocaudal and oblique images of 150 suspicious mammograms from 150 patients that were histologically proven to be malignant were analysed using the Second Look CAD (CADx Medical Systems, Quebec, Canada). Cases were selected randomly using the clinic's internal tumour case sampler. Correct marking of the malignant lesion in at least one view was scored as a true positive. Marks not at the location of the malignant lesion were scored as false positives. In addition, mammograms with histologically proven benign masses ( n=50) and microcalcifications ( n=50), as well as 100 non-suspicious mammograms, were scanned in order to determine the value of false-positive marks per image. The 150 mammograms included 94 lesions that were suspicious due to masses, 26 due to microcalcifications and 30 showed both signs of malignancy. The overall sensitivity was 90.0% (135 of 150). Sensitivity on subsets of the data was 88.7% (110 of 124) for suspicious masses (MA) and 98.2% (55 of 56) for microcalcifications. Eight of 14 false-negative cases were large lesions. The overall false-positive rate was observed as 0.28 and 0.97 marks per image of microcalcifications and masses, respectively. The lowest false-positive rates for microcalcifications and MA were observed in the cancer subgroup, whereas the highest false-positive rates were scored in the benign but mammographically suspicious subgroups, respectively. The new CAD system shows a high tumour detection rate, with approximately 1.3 false positive marks per image. These results suggest that this system might be clinically useful as a second reader of mammograms. The system performance was particularly useful for detecting microcalcifications.     

An automated detection of lacunar infarct regions in brain MR images: preliminary study

The purpose of this study is to develop a technique to detect lacunar infarct regions automatically in brain MR images. Our detection method is based on the definition of lacunar infarcts. After inputted images were binarized, we used feature values such as area, circularities and the center of gravity of candidate regions to extract isolated lacunar infarct regions. We also developed and used a new filter to enhance the signals of lacunar infarcts adjacent to some high intensity regions. 10 cases involving 81 sectional images were applied to our experiment. As a result, the sensitivity was 100% with approximately 1.77 false-positives per image. Our results are promising on the first stage, although it remains to improve on problems that to eliminate false-positives and automatically establish threshold value.     

Optimization of X-ray mammography and technetium-99m methoxyisobutylisonitrile scintimammography in the diagnosis of non-palpable breast lesions

The purpose of this study was to evaluate the contribution of technetium-99m methoxyisobutylisonitrile (MIBI) scintimammography to the early diagnosis of breast cancer in 78 patients with non-palpable breast lesions detected by mammography. In all cases biopsy was indicated and they were classified into three groups according to the mammographic findings: high (28), intermediate (30) and low (20) mammographic probability of malignancy. Histological diagnosis confirmed 37 benign and 41 malignant lesions. In the high-probability group ^99mTc-MIBI scintimammography changed the four false-positives into true negatives at the expense of two false-negatives; in the intermediate group it changed nine of the 17 false-positives into true-negatives at the expense of one false-negative, and in the low-probability group it changed five of the 16 false-positives into true-negatives without false-negatives. Applying scintimammography to patients included in the intermediate and low-probability groups together, 14 of the 33 mammographic false-positives were changed into true-negatives with 1 false-negative; thus, 41% of the unnecessary biopsies would have been avoided. When MIBI scintimammography was applied to the low-probability group, the negative predictive value was 100% and the unnecessary biopsies would have been reduced by 31%. Received 10 November 1997 and in revised form 19 January 1998     

Multimodality computerized diagnosis of breast lesions using mammography and sonography

RATIONALE AND OBJECTIVES: The purpose of this study is to investigate the use of computer-extracted features of lesions imaged by means of two modalities, mammography and breast ultrasound, in the computerized classification of breast lesions. MATERIAL AND METHODS: We performed computerized analysis on a database of 97 patients with a total of 100 lesions (40 malignant, 40 benign solid, and 20 cystic lesions). Mammograms and ultrasound images were available for these breast lesions. There was an average of three mammographic images and two ultrasound images per lesion. Based on seed points indicated by a radiologist, the computer automatically segmented lesions from the parenchymal background and automatically extracted a set of characteristic features for each lesion. For each feature, its value averaged over all images pertaining to a given lesion was input to a Bayesian neural network for classification. We also investigated different approaches to combine image-based features into this by-lesion analysis. In that analysis, mean, maximum, and minimum feature values were considered for all images representing a lesion. We considered performance by using a leave-one-lesion-out approach, based on image features from mammography alone (two to five features), ultrasound alone (three to four features), and a combination of features from both modalities (three to five features total). RESULTS: For the classification task of distinguishing cancer from other abnormalities in a lesion-based analysis by using a single modality, areas under the receiver operating characteristic curves (A(z) values) increased significantly when the computer selected the manner (mean, minimum, or maximum) in which image-based features were combined into lesion-based features. The highest performance was found for lesion-based analysis and automated feature selection from mean, maximum, and minimum values of features from both modalities (resulting in a total of four features being used). That A(z) value for the task of distinguishing cancer was 0.92, showing a statistically significant increase over that achieved with features from either mammography or ultrasound alone. CONCLUSION: Computerized classification of cancer significantly improved when lesion features from both modalities were combined. Classification performance depended on specific methods for combining features from multiple images per lesion. These results are encouraging and warrant further exploration of computerized methods for multimodality imaging.     

The correlation of mammographic-and histologic patterns of breast cancers in BRCA1 gene mutation carriers, compared to age-matched sporadic controls

Breast cancers in BRCA1 gene mutation carriers often have specific histologic features: grade III tumors with pushing margins. Our purpose was to compare the mammographic and histologic features of breast cancers in carriers with those in age-matched sporadic controls. The features of breast cancers in 27 BRCA1 carriers found during annual surveillance were compared to those in 107 age-matched sporadic controls. The carriers had no (classic) spiculated mammographic lesions, a high percentage of well-defined masses and hardly any masses with microcalcifications, whereas the controls had significantly fewer well-defined ones and only in 27% spiculated lesions on the mammogram. The well-defined mammographic tumors correlated in 83% of the carriers and in 70% of the controls with histologic circumscribed tumor margins. Spiculated mammographic lesions in the controls were in 90% grade I or II tumors. DCIS with or without infiltration was seen in 22% of the carriers and in 45% of the controls. In conclusion, breast cancers diagnosed in BRCA1 carriers do not have classic malignant mammographic features. A minority of the young sporadic controls show the classic malignant lesion on the mammogram. Both carriers and controls generally show a good correlation between their mammographic- and histologic tumor pattern.     

乳腺癌的不同病理类型的表现与X线征象分析

目的探讨乳腺癌的X线表现特征,提高诊断水平。方法回顾性分析经手术病理证实的174个乳腺癌的X线表现特征并与病理分型对照。结果乳腺导管内癌X线特征为单纯微钙化、小肿块;浸润性导管癌为肿块影,尤其是毛刺样肿块合并钙化为其特征性表现;浸润性小叶癌表现为不合并簇状微钙化的肿块;粘液癌表现为边界清楚之较大肿块。结论乳腺癌不同病理组织学类型的X线表现特征不同,根据X线表现可推测乳腺癌的病理分型。     

Tubular carcinoma of the breast: mammographic, sonographic, clinical and pathologic findings

PURPOSE: To determine and quantitate the radiological characteristics of tubular carcinoma of the breast, to report clinical and pathologic findings and to define findings at follow-up. MATERIALS AND METHODS: A retrospective review of records of 2872 women who received a diagnosis of breast carcinoma between January 1988 and January 2006 revealed 32 histopathologically proven pure tubular carcinoma of the breast. Analysis included history; findings at physical examination, mammography, and sonography (US) at the time of diagnosis and in postoperative follow-up and histopathological results. RESULTS: Fifty-nine percent of the patients (n=19) presented with a palpable mass. The mammographic findings were a mass in 23 (72%), a mass with microcalcifications in 2 (6%), asymmetric focal density in 1 (3%), architectural distortion in 1 (3%) and negative in 5 (16%) of the 32 patients. Most (96%) masses had spiculated margins. US depicted 30 masses in 29 patients, all of which were hypoechoic, mostly (n=27, 90%) with posterior acoustic shadowing. The cancer was clinically occult in 41% (n=13), mammographically occult in 16% (n=5), and sonographically occult in 6% (n=2) of the patients. Histologically, the tumor was multifocal in 3% (n=1) of the patients. Four (13%) patients developed contralateral breast carcinoma at follow-up. CONCLUSION: Tubular carcinoma has a variety of presentations, but it is mostly seen on mammography as a small spiculated mass, and on sonography as an irregular mass with posterior acoustic shadowing. Although tubular carcinoma is known as a well-differentiated tumor with excellent prognosis, the mammographic follow-up of the contralateral breast is important.     

Mammographic,sonographic and MR imaging features of invasive micropapillary breast cancer

Purpose

Describe mammographic, sonographic and MRI findings of invasive micropapillary carcinoma (IMPC) of the breast.

Materials and methods

Review of the pathology database identified 43 patients (mean age, 59.3 years) with the diagnosis of breast IMPC. Three patients had no available imaging studies. Mammograms (40), breast ultrasounds (33) and MRIs (8) were retrospectively evaluated by two radiologists in consensus following the BI-RADS Lexicon. Clinical, histopathologic features, as well as hormone status were recorded.

Results

Twenty patients presented with palpable abnormality (20/40, 50%). Thirty-five patients had an abnormal mammogram (87.5%, 35/40) showing 39 lesions, 29 corresponding to masses (29/39, 74.4%), 11 associated with microcalcifications and two associated with architectural distortion. Sonography identified 41 masses (in 33 patients) displaying an irregular shape (30/41, 73.2%), appearing hypoechoic (39/41, 95%), with spiculated or angular margins (26/41, 63.4%), non-parallel orientation (26/41, 63.4%) and combined acoustic posterior pattern (18/41, 44%). MRI identified 13 lesions (in eight patients), 12 as masses (12/13, 92.3%) with irregular or spiculated margins (12/12, 100%), eight displaying an irregular or lobulated shape (8/12, 66.7%), six with homogeneous internal enhancement (6/12, 50%) and eight with type 3 enhancement curve (8/12, 61.5%). Associated non-mass like enhancement was noted in two patients. Twenty-nine patients had associated lymphovascular invasion (29/40, 72.5%) and axillary lymph node metastases were present in 22 of the 39 patients (22/39, 56%).

Conclusion

Invasive ductal carcinoma with IMPC features display imaging findings highly suspicious of malignant lesions. They are associated with high lymphovascular invasion and lymph node metastases rates.     

原发性乳腺淋巴瘤X线表现及与病理相关性探讨

目的探讨原发性乳腺淋巴瘤钼靶X线表现特征及其与病理相关性，提高影像医师对该病的认识。方法回顾性复习27例经手术病理证实的原发性乳腺淋巴瘤，其中术前有完整钼靶X线资料的患者为14例。对该14例患者的临床、钼靶X线表现特征及病理进行了回顾性分析。患者均为女性，年龄28～56岁。右乳7例，左乳6例，双乳1例。结果14例原发性乳腺淋巴瘤中，13例为非霍奇金淋巴瘤(Non-Hodgkin lymphoma，NHL)(弥漫型12例，结节型1例)，1例为霍奇金淋巴瘤(Hodgkin lymphoma，HL)。乳腺X线片上9例表现为单乳单发肿块，2例表现为单乳多发肿块，1例为双乳多发肿块，2例表现为单侧乳腺致密浸润伴皮肤增厚。X线片上共发现18个肿块，肿块直径从0．7—5．0cm，平均直径2．6cm，其中13个肿块边缘清楚，5个肿块边缘表现为部分清楚部分不清楚，均未见毛刺、钙化或漏斗征及皮肤凹陷征等乳腺癌典型X线征象。此外，该组资料显示淋巴瘤X线表现与其组织病理学类型无相关性。结论原发性乳腺淋巴瘤的临床及影像学表现缺乏特异性，最后诊断需依靠病理学确诊。如临床乳腺检查考虑恶性且伴有腋下肿大淋巴结，而X线征象表现为良性或不典型乳腺癌者应提示除有不典型髓样癌可能外，还应考虑到淋巴瘤，及时做针吸或切取活检，有利于临床选取恰当的治疗方案。     

Differentiation of phyllodes tumors versus fibroadenomas

Purpose: To determine if mammographic and sonographic findings allow discrimination between phyllodes tumor and large sized fibroadenoma, which mimic each other in the clinical, radiologic and histopathologic appearances.Material and Methods: Thirty-one histopathologically proven masses including 12 phyllodes tumors and 19 fibroadenomas 3 cm or greater in diameter were compared. In total 28 women were retrospectively evaluated by mammography and pre-operative sonography.Results: Mammography revealed a high-density mass compared with surrounding fibroglandular breast tissue to be present in 9 of the 12 (75%) phyllodes tumors and 7 of the 19 (37%) fibroadenomas. At sonography a mass, which had a round or lobulated shape, marked posterior acoustic enhancement and intramural cystic areas, were statistically significantly more likely to be phyllodes tumors than fibroadenomas. None of the other mammographic or sonographic characteristics proved to be useful in differentiating phyllodes tumors and fibroadenomas.Conclusion: Although masses of high density at mammography, circumscribed border associated with posterior acoustic enhancement and internal cystic areas at sonography should suggest the diagnosis of phyllodes tumors rather than large sized fibroadenomas, there was a substantial overlap in the mammographic and sonographic characteristics of these two tumors. Therefore, an excisional biopsy would be necessary for equivocal masses.